Electric Vehicle Supply Equipment Location and Capacity Allocation for Fixed-Route Networks

Electric vehicle (EV) supply equipment location and allocation (EVSELCA) problems for freight vehicles are becoming more important because of the trending electrification shift. Some previous works address EV charger location and vehicle routing problems simultaneously by generating vehicle routes from scratch. Although such routes can be efficient, introducing new routes may violate practical constraints, such as drive schedules, and satisfying electrification requirements can require dramatically altering existing routes. To address the challenges in the prevailing adoption scheme, we approach the problem from a fixed-route perspective. We develop a mixed-integer linear program, a clustering approach, and a metaheuristic solution method using a genetic algorithm (GA) to solve the EVSELCA problem. The clustering approach simplifies the problem by grouping customers into clusters, while the GA generates solutions that are shown to be nearly optimal for small problem cases. A case study examines how charger costs, energy costs, the value of time (VOT), and battery capacity impact the cost of the EVSELCA. Charger costs were found to be the most significant component in the objective function, with an 80\% decrease resulting in a 25\% cost reduction. VOT costs decrease substantially as energy costs increase. The number of fast chargers increases as VOT doubles. Longer EV ranges decrease total costs up to a certain point, beyond which the decrease in total costs is negligible

Synthesis Study of Best Practices for Mapping and Coordinating Detours for Maintenance of Traffic (MOT) and Risk Assessment for Duration of Traffic Control Activities

Maintenance of traffic (MOT) during construction periods is critical to the success of project delivery and the overall mission of transportation agencies. MOT plans may include full road closures and coordination of detours near construction areas. Various state DOTs have designed their own manuals for detour mapping and coordination. However, very limited information is provided to select optimal detour routes. Moreover, closures or detours should provide not only measurable consequences, such as vehicle operating costs and added travel time, but also various unforeseen qualitative impacts, such as business impacts and inconvenience to local communities. Since the qualitative aspects are not easily measurable they tend to be neglected in systematic evaluations and decision-making processes. In this study, the current practices obtained based on an extensive literature review, a nation-wide survey, as well as a series of interviews with INDOT and other state DOTs are leveraged to (1) identify a comprehensive set of Key Performance Indicators (KPIs) for detour route mapping, (2) understand how other state DOTs address the qualitative criteria, (3) identify how the involved risks during the planning, service time, and closure of the detour routes are managed, and (4) recommend process improvements for INDOT detour mapping guidelines. As demonstrated by two sample case studies, the proposed KPIs can be taken as a basis for developing a decision-support tool that enables decision-makers to consider both qualitative and quantitative aspects for optimal detour route mapping. In addition, the current INDOT detour policy can be updated based on the proposed process improvements

Examining the persistence of telecommuting after the COVID-19 pandemic

This study focuses on the long-term impacts of COVID-19 on telecommuting behavior. We seek to study the future of telecommuting, in the post-pandemic era, by capturing the evolution of observed behavior during the COVID-19 pandemic. To do so, we implemented a comprehensive multi-wave nationwide panel survey (the Future Survey) in the U.S. throughout 2020 and 2021. A panel Generalized Structural Equation Model (GSEM) was used to investigate the effects of two perceptual factors on telecommuting behavior: (1) perceived risk of COVID-19; and (2) perceived telecommuting productivity. The findings of this study reveal significant and positive impacts of productivity and COVID-risk perception on telecommuting behavior. Moreover, the findings indicate a potential shift in preferences toward telecommuting in the post-pandemic era for millennials, employees with long commute times, high-income, and highly educated employees. Overall, a potential increase in telecommuting frequency is expected in the post-pandemic era, with differences across socio-economic groups

Analyzing the Impacts of a Successful Diffusion of Shared E-Scooters and Other Micromobility Devices and Efficient Management Strategies for Successful Operations in Illinois

Active transportation can play an important role in promoting more physically active and positive public health outcomes. While walking and biking provide significant physical health benefits, their modal share remains low. As a new form of micromobility service, shared e-scooters can enhance the suite of options available in cities to promote active transportation and fill in the gaps when walking or biking are not preferred. Although e-scooters show potential as a mode of transportation, it is unclear whether people will adopt the technology for everyday use. Furthermore, shared micromobility (e.g., electric scooters) is gaining attention as a complementary mode to public transit and is expected to offer a solution to access/egress for public transit. However, few studies have analyzed integrated usage of shared e-scooters and public transit systems while using panel data to measure spatial and temporal characteristics. This study aims to examine the adoption and frequency of shared e-scooter usage and provide policy implementation. To do so, the researchers launched a survey in the Chicago region in late 2020 and collected a rich data set that includes residents’ sociodemographic details and frequency of shared e-scooter use. To characterize the frequency, the researchers used an ordered probit structure. The findings show that respondents who are male, low income, Millennials and Generation Z, or do not have a vehicle are associated with a higher frequency of shared e-scooter use. Furthermore, this study utilizes shared e-scooter trips for a 35-day measurement period from 10 shared e-scooter operators in Chicago, where the researchers used a random-parameter negative binomial modeling approach to analyze panel effects. The findings highlight the critical role of spatial and temporal characteristics in the integration of shared e-scooters with transit.IDOT-R27-215Ope

Facilitating Electric-Propulsion of Autonomous Vehicles Through Efficient Design of a Charging-Facility Network

69A3551747105Electric vehicles, autonomous or manual, provide a valuable opportunity to address issues of environmental pollution, climate change, and national security. In recognition of the synergies between vehicle electrification and autonomy, this study addresses the facilitation of vehicle electrification in the prospective future era where autonomous vehicles become mainstream. Part 1 of this study proposes a methodology for scheduling deployment of electric charging facilities (charging guideways and charging stations) at AV dedicated lanes over candidate links of a road network over a long-term horizon period. The methodology is intended to assist highway agencies in decision support regarding the scheduling, locations, and operating capacities of the EV charging facilities, where the road users (travelers) minimize their travel times by selecting routes and their preferred vehicle type (AV vs. HDV). The bi-level model is solved using a Genetic Algorithm, and the results provide insights into the impacts of alternative scenarios of charging infrastructure investment. Part 2 of the study presents a methodology for environmentally sustainable electric charging station deployment, such that travelers experience a smooth shift from ICEVs to EVs over a lengthy planning horizon

Location Planning for Electric Charging Stations and Wireless Facilities in the Era of Autonomous Vehicle Operations

The emergence of Autonomous Vehicles (AVs) provides a valuable opportunity to reduce greenhouse gas emissions by improving traffic mobility. Due to AV-EV synergies, AVs will be likely introduced into the market when the Electric Vehicle (EV) market share is high. Hence, future AVs are expected to be electric, and it is anticipated that Autonomous Electric Vehicles (AEVs) will help address climate change and environmental pollution. This is the expectation particularly during the transition phase where mixed AV-HDV fleet will require lane management policies such as AV-exclusive lane. The possibility of installing wireless charging facility at AVexclusive lanes is expected to motivate great patronage of AVs. This thesis proposes a planning framework for AEV charging. The framework is intended to help transportation decision-makers determine EV charging facility locations and capacities for the mixed fleet of AV and HDV. The bi-level nature of the framework captures the decision-making processes of the transportation agency decision-makers and travelers, thereby providing solid theoretical and practical foundations for the EV charging network design. At the upper level, the decision-makers seek to determine the locations and operating capacities of the EV charging facilities, in a manner that minimizes total travel time and construction costs subject to budgetary limitations. In addition, the transportation decision-makers provide AV-exclusive lanes to encourage AV users to reduce travel time, particularly at wireless-charging lanes, as well as other reasons, including safety. At the lower level, the travelers seek to minimize their travel time by selecting their preferred vehicle type (AV vs. HDV) and route. In measuring the users delay costs, the thesis considered network user equilibrium because the framework is designed for urban networks where travelers route choice affects their travel time. The bi-level model is solved using the Non-Dominated Sorting Genetic Algorithm (NSGA-II) algorithm. The results of the numerical experiments suggest that for a higher weight ratio of user cost dollar to agency cost dollar, the optimal deployment plan will include a greater number of wireless-charging facilities. Furthermore, the results suggest that, compared to the scenario where the transport decision-makers construct charging stations and where construct wireless-charging facilities, the scenario where the transport decision-makers construct both of them, the total costs decrease by 49% and 11%, respectively. It is shown that enabling wireless-charging facilities at both AV-exclusive and general-purpose lanes can reduce total cost by 16% and 21% compared to plan where wireless-charging facilities are provided only at AV-exclusive and where are provided only at general-purpose lanes, respectively

Medium-duty Electric Vehicle Infrastructure Planning and Operations in Urban Transportation Networks

This dissertation focuses on the electrification of freight and transit vehicles as a sustainable solution to mitigate greenhouse gas emissions. The primary objectives of this dissertation focuses on efficiently addressing Electric Vehicle Supply Equipment Location and Capacity Allocation (EVSELCA) problems, optimizing Single Depot Electric Vehicle Scheduling Problem (SDEVSP) for urban transit systems, and optimizing the Electric Bus Scheduling and Charger Location (EBSCL). By addressing these objectives, this dissertation aims to contribute to the advancement of sustainable and environmentally responsible transportation in the context of heavy electric vehicles. In this regard, first, this dissertation introduces a mixed-integer linear programming (MILP) model for EVSELCA. This model optimizes the locations and number and type of chargers, aiming to minimize strategic investment costs. Second, the research proposes a two-step solution to SDEVSP. In the first step, an integer programming model generates blocks of consecutive trips. The second step introduces an MILP, which involves chaining these blocks to create efficient bus runs, optimizing recharging between blocks, and ensuring next day operability constraints are satisfied. Finally, the dissertation presents an integrated model that optimizes both electric vehicle scheduling and charger location. Each developed model undergoes computational performance analysis, and a comprehensive case study is designed to provide key managerial and policy insights, along with extensive sensitivity analyses aiming to identify crucial parametric levers

Best Practice Operation of Reversible Express Lanes for the Kennedy Expressway

Reversible lanes in Chicago’s Kennedy Expressway are an available infrastructure that can significantly improve traffic performance; however, a special focus on congestion management is required to improve their operation. This research project aims to evaluate and improve the operation of reversible lanes in the Kennedy Expressway. The Kennedy Expressway is a nearly 18-mile-long freeway in Chicago, Illinois, that connects in the southeast to northwest direction between the West Loop and O’Hare International Airport. There are two approximately 8-mile reversible lanes in the Kennedy Expressway’s median, where I-94 merges into I-90, and there are three entrance gates in each direction of this corridor. The purpose of the reversible lanes is to help the congested direction of the Kennedy Expressway increase its traffic flow and decrease the delay in the whole corridor. Currently, experts in a control location switch the direction of the reversible lanes two to three times per day by observing real-time traffic conditions captured by a traffic surveillance camera. In general, inbound gates are opened and outbound gates are closed around midnight because morning traffic is usually heavier toward the central city neighborhoods. In contrast, evening peak-hour traffic is usually heavier toward the outbound direction, so the direction of the reversible lanes is switched from inbound to outbound around noon. This study evaluates the Kennedy Expressway’s current reversing operation. Different indices are generated for the corridor to measure the reversible lanes’ performance, and a data-driven approach is selected to find the best time to start the operation. Subsequently, real-time and offline instruction for the operation of the reversible lanes is provided through employing deep learning and statistical techniques. In addition, an offline timetable is also provided through an optimization technique. Eventually, integration of the data-driven and optimization techniques results in the best practice operation of the reversible lanes.IDOT-ICT-195Ope